Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Xun Wang; Yinghui Tan; Tao Li; Chuang Liu; Guanghao Yang; Qian Wang

doi:10.1117/12.3014395

9 January 2024 Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, Qian Wang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129690Y (2024) https://doi.org/10.1117/12.3014395
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In order to understand the multidimensional situation prediction of digital twin active power grid, a research on multidimensional situation prediction of digital twin active power grid based on LSTM algorithm is proposed. In this paper, firstly, a multi-dimensional situation prediction algorithm of power grid key indicators based on LSTM is established to realize the change prediction of key indicators attributes of digital twin active power grid. Secondly, the data of several key indicators such as load characteristics are collected, and a multi-dimensional system prediction model is established, which can control the state of active power grid; The LSTM prediction algorithm is proposed to fit the characteristics of multi-dimensional data, and the next stage of multi-dimensional data prediction is mapped to the power digital twin, so as to realize the synchronous implementation and intelligent regulation of smart energy system operation planning. Finally, a simulation test model is established, and an example shows that the multi-dimensional situation prediction method of digital twin power grid based on deep learning can better predict and distinguish the power grid situation, and provide decision support for accurate planning of energy system in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, and Qian Wang "Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129690Y (9 January 2024); https://doi.org/10.1117/12.3014395

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KEYWORDS

Power grids

Data modeling

Education and training

Data transmission

Data storage

Instrument modeling

Sampling rates

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